In the past, researchers needed either supercomputers or large computer clusters to run simulations. Or they had to be content to run only a tiny fraction of the process on their desktop computers. But a new open-source software package developed at Stanford University is making it possible to do complex simulations of molecular motion on desktop computers at much faster speeds than has been previously possible.
"Simulations that used to take three years can now be completed in a few days," said Vijay Pande, an associate professor of chemistry at Stanford University and principal investigator of the Open Molecular Mechanics (OpenMM) project. "With this first release of OpenMM, we focused on small molecular systems simulated and saw speedups of 100 times faster than before."
OpenMM is a collaborative project between Pande's lab and Simbios, the National Center for Physics-based Simulation of Biological Structures at Stanford, which is supported by the National Institutes of Health. The project is described in a paper that was scheduled to be posted online Feb. 3 in the "Early View" section of the Journal of Computational Chemistry.
The key to the accelerated simulations OpenMM makes possible is the advantage it takes of current graphics processors (GPUs), which cost just a few hundred dollars. At its core, OpenMM makes use of GPU acceleration, a set of advanced hardware and software technologies that enable GPUs, working in concert with the system's central processor (CPU), to accelerate applications beyond just creating or manipulating graphics.
The icing on the molecular-simulation cake is that the software has no allegiance to any particular brand of GPU, meaning it is, as computer geeks like to say, "brand agnostic." OpenMM will enable molecular dynamics (MD) simulations to work on most of the high-end GPUs used today in laptop and desktop computers.
This is a boon to MD developers. Converting their code to run on just one GPU product is a challenging project by itself. And until now, if developers wanted to accelerate their MD software on different brands of GPUs, they would have to write multiple versions of their code. OpenMM provides a common interface.
"OpenMM will allow researchers to focus on the science at hand instead of the hardware," Pande said. "Researchers will see a jump in productivity and resourcefulness from computers they already own." With OpenMM, researchers can use GPUs to perform massively parallel calculations.
OpenMM fits squarely with Simbios' mission of providing computational tools to stimulate research in biology and medicine, according to Russ Altman, principal investigator of Simbios and chair of the Department of Bioengineering at Stanford. "OpenMM will be a tool that unifies the MD community," he said. "Instead of difficult, disparate efforts to recode existing MD packages to enjoy the speedups provided by GPUs, OpenMM will bring GPUs to existing packages and allow researchers to focus on discovery."
The new release of OpenMM includes a version of the widely used MD package GROMACS that integrates the OpenMM library, enabling it to be sped up on high-end NVIDIA and AMD/ATI graphics cards. Close collaborations with AMD (which owns the ATI brand) and NVIDIA were critical for getting OpenMM to run on their GPUs.
"Cross-platform solutions like OpenMM enable a much broader community of researchers to leverage GPU acceleration capabilities like ATI Stream technology" said Patricia Harrell, director of Stream Computing, AMD. "AMD is committed to supporting open, cross platform tools that allow researchers to focus on solving problems with their GPU of choice."
NVIDIA is similarly committed to OpenMM. "OpenMM promises to further increase the adoption of GPU technology among the molecular dynamics community," said Andy Keane, general manager, GPU Computing at NVIDIA. "We'll continue our close collaboration with Stanford on OpenMM so that current and future libraries can maximally leverage the power of the GPU."
OpenMM incorporates specially developed algorithms that allow MD software to take full advantage of the GPU architecture. In fact, the OpenMM code is at the heart of the GPU implementations of the Folding@home project, which uses the horsepower of GPUs and CPUs in computers around the world to simulate protein folding. The current release uses an implicit solvent model, in which all the surrounding fluid, such as water, is represented as one continuous medium, rather than having each water molecule represented individually (an explicit solvent model). Future releases will allow the modeling of explicit solvent.
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